Social Achievement and Centrality in MathOverflow

  • Leydi Viviana MontoyaEmail author
  • Athen Ma
  • Raúl J. Mondragón
Part of the Studies in Computational Intelligence book series (SCI, volume 476)


This paper presents an academic web community, MathOverflow, as a network. Social network analysis is used to examine the interactions among users over a period of two and a half years.We describe relevant aspects associated with its behaviour as a result of the dynamics arisen from users participation and contribution, such as the existence of clusters, rich–club and collaborative properties within the network.We examine, in particular, the relationship between the social achievements obtained by users and node centrality derived from interactions through posting questions, answers and comments. Our study shows that the two aspects have a strong direct correlation; and active participation in the forum seems to be the most effective way to gain social recognition.


Degree Distribution Social Network Analysis Betweenness Centrality Geodesic Distance Closeness Centrality 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Rodrigues, E., Milic-Frayling, N., Fortuna, B.: Social tagging behaviour in community-driven question answering. In: IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2008, vol. 1, pp. 112–119. IEEE (2008)Google Scholar
  2. 2.
    Mendes Rodrigues, E., Milic-Frayling, N.: Socializing or knowledge sharing?: characterizing social intent in community question answering. In: Proceedings of the 18th ACM Conference on Information and Knowledge Management, pp. 1127–1136. ACM (2009)Google Scholar
  3. 3.
    Tausczik, Y., Pennebaker, J.: Predicting the perceived quality of online mathematics contributions from users’ reputations. In: Proceedings of the 2011 Annual Conference on Human Factors in Computing Systems, pp. 1885–1888. ACM (2011)Google Scholar
  4. 4.
    Burel, G., He, Y., Alani, H.: Automatic identification of best answers in online enquiry communities. The Semantic Web: Research and Applications, 514–529 (2012)Google Scholar
  5. 5.
    Tausczik, Y., Pennebaker, J.: Participation in an online mathematics community: differentiating motivations to add. In: Proceedings of the ACM 2012 Conference on Computer Supported Cooperative Work, pp. 207–216. ACM (2012)Google Scholar
  6. 6.
    Joel, S.: Cultural anthropology of stack exchange. Hacker News London Meetup - Events (June 2012),
  7. 7.
    MathOverflow: Dumps files (April 1, 2012),
  8. 8.
    Newman, M.: The mathematics of networks. Electronic Article (2005),
  9. 9.
    Newman, M.: The structure of scientific collaboration networks. Proceedings of the National Academy of Sciences 98(2), 404–409 (2001)MathSciNetzbMATHCrossRefGoogle Scholar
  10. 10.
    Newman, M.: The structure and function of complex networks. SIAM Review 45(2), 167–256 (2003)MathSciNetzbMATHCrossRefGoogle Scholar
  11. 11.
    Faust, K.: Comparing social networks: size, density, and local structure. Metodološki zvezki 3(2), 185–216 (2006)Google Scholar
  12. 12.
    Pastor-Satorras, R., Vázquez, A., Vespignani, A.: Dynamical and correlation properties of the internet. Physical Review Letters 87(25), 258701 (2001)CrossRefGoogle Scholar
  13. 13.
    Latora, V., Marchiori, M.: Efficient behavior of small-world networks. Physical Review Letters 87(19), 198701 (2001)CrossRefGoogle Scholar
  14. 14.
    Zhou, S., Mondragón, R.J.: The rich-club phenomenon in the internet topology. IEEE Communications Letters 8(3), 180–182 (2004)CrossRefGoogle Scholar
  15. 15.
    Colizza, V., Flammini, A., Serrano, M., Vespignani, A.: Detecting rich-club ordering in complex networks. Nature Physics 2(2), 110–115 (2006)CrossRefGoogle Scholar
  16. 16.
    Jiang, Z., Zhou, W.: Statistical significance of the rich-club phenomenon in complex networks. New Journal of Physics 10(4), 043002 (2008)Google Scholar
  17. 17.
    Xu, X., Zhang, J., Small, M.: Rich-club connectivity dominates assortativity and transitivity of complex networks. Physical Review E 82(4), 046117 (2010)Google Scholar
  18. 18.
    MathOverflow: Mathoverflow - frequently asked questions. Forum web page (July 2012),
  19. 19.
    Wasserman, S., Faust, K.: Social network analysis: Methods and applications, vol. 8. Cambridge University Press (1994)Google Scholar
  20. 20.
    Owen, A., Petrie, M., Palipana, A., Green, D., Croft, T., Jones, A., Joiner, S.: Spearman’s correlation. Loughborough University (2012),
  21. 21.
    s.n.: Ib geography notes. Web page (2012),
  22. 22.
    Hossain, L., Wu, A.: Communications network centrality correlates to organisational coordination. International Journal of Project Management 27(8), 795–811 (2009)CrossRefGoogle Scholar
  23. 23.
    Lund, A., Lund, M.: Spearman’s rank-order correlation. Web page (2012),
  24. 24.
    Zar, J.: Significance testing of the spearman rank correlation coefficient. Journal of the American Statistical Association 67(339), 578–580 (1972)CrossRefGoogle Scholar
  25. 25.
    Tan, P., Steinbach, M., Kumar, V.: Introduction to data mining. Pearson Addison Wesley (2006)Google Scholar
  26. 26.
    Clauset, A., Newman, M., Moore, C.: Finding community structure in very large networks. Physical Review E 70(6), 066111 (2004)Google Scholar
  27. 27.
    Newman, M.: Fast algorithm for detecting community structure in networks. Physical Review E 69(6), 066133 (2004)Google Scholar
  28. 28.
    Xu, G., Zhang, Y., Li, L.: Web Mining and Social Networking: Techniques and Applications, vol. 6. Springer (2010)Google Scholar
  29. 29.
    Foundation, S.M.R.: Nodexl excel template. Computer Program (2012),

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© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Leydi Viviana Montoya
    • 1
    Email author
  • Athen Ma
    • 1
  • Raúl J. Mondragón
    • 1
  1. 1.Queen Mary University of LondonLondonUK

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